fullC/code/omol_store.py

"""Zarr v3 shard writer for Pass A.

Layout
------
Each worker writes self-contained *shards*, so there is no write contention and no resize logic:

    store/p1/<dataset>/shard_<nnnn>_<nn>.zarr   scalars + per-atom + per-pair
    store/p2/<dataset>/shard_<nnnn>_<nn>.zarr   the same, plus fock/, eps/, occ/

p2 duplicates the p1 arrays on purpose: the tables are tiny next to the matrices (<0.2 TB for the
whole collection) and it keeps p1 independently usable without the 6 TB matrix store.

Columns are *packed*: all float scalars live in one (n_calc, n_col) array, all per-atom floats in
one (n_atom_total, n_col) array, and so on, with the column names recorded in group attrs. Writing
one array per column meant ~80 Zarr arrays per shard, and zarr-python's sync wrapper costs enough
per array that shard writes dominated the run; packing cuts that to ~15 arrays.

Ragged quantities are a concatenated array plus an int64 offsets array of length n_calc+1, so
calculation i occupies [off[i], off[i+1]).

Codecs follow the benchmark: Blosc zstd 9 + bit-shuffle for the int32 Fock triangles (4.5x), Blosc
zstd 5 + byte-shuffle elsewhere.
"""
from __future__ import annotations
import os
import numpy as np
import zarr
from zarr.codecs import BloscCodec

SHELLS = ("s", "p", "d", "f", "g")

FOCK_CODEC = [BloscCodec(cname="zstd", clevel=9, shuffle="bitshuffle")]
DATA_CODEC = [BloscCodec(cname="zstd", clevel=5, shuffle="shuffle")]

SCALARS_F8 = (
    "e_total", "e_total_engrad", "e_nuc_rep", "e_one_elec", "e_two_elec", "e_kinetic",
    "virial_ratio", "e_xc", "e_nl", "e_exchange", "n_alpha_int", "n_beta_int",
    "s2", "s2_ideal", "s2_dev", "conv_denergy", "conv_maxdp", "conv_rmsdp", "conv_diiserr",
    "smallest_ovlp_eig", "grad_norm", "grad_rms", "grad_max", "run_time_s",
    "dipole_au", "dipole_debye", "quad_iso", "npa_core", "npa_valence", "npa_rydberg",
    "nbo_lewis", "nbo_nonlewis", "homo_a", "lumo_a", "gap_a", "homo_b", "lumo_b", "gap_b",
)
SCALARS_I = ("charge", "mult", "nelec", "nbas", "naux", "n_lindep", "scf_cycles", "n_atoms")
FLAGS = ("scf_converged", "terminated_normally", "nbo_available", "npa_available",
         "is_uhf", "has_fock")
VEC = (("dipole_elec", 3), ("dipole_nuc", 3), ("dipole_total", 3), ("rot_const_cm", 3),
       ("rot_const_mhz", 3), ("quad_diag", 3), ("quad_nuc", 6), ("quad_elec", 6),
       ("quad_total", 6))

ATOM_1D = ("mulliken_q", "mulliken_s", "loewdin_q", "loewdin_s",
           "mayer_NA", "mayer_ZA", "mayer_QA", "mayer_VA", "mayer_BVA", "mayer_FA",
           "npa_q", "npa_atom_core", "npa_atom_val", "npa_atom_ryd", "npa_spin")
ATOM_VEC3 = ("coords", "forces")
ATOM_SHELL = ("mulliken_shell_q", "mulliken_shell_s", "loewdin_shell_q", "loewdin_shell_s",
              "natural_config")
PAIRS = ("mayer_bo", "loewdin_bo", "mulliken_ovlp")

ATOM_F8_COLS = [f"{k}_{ax}" for k in ATOM_VEC3 for ax in "xyz"] + list(ATOM_1D)
VEC_COLS = [f"{name}_{i}" for name, w in VEC for i in range(w)]
SHELL_COLS = [f"{k}_{sh}" for k in ATOM_SHELL for sh in SHELLS]


def frontier(eps, occ):
    """HOMO, LUMO and gap in Eh. Orbitals removed for linear dependence print as exactly 0.0."""
    if eps is None or occ is None or len(eps) == 0:
        return np.nan, np.nan, np.nan
    occupied = np.flatnonzero(occ > 0)
    if occupied.size == 0:
        return np.nan, np.nan, np.nan
    h = int(occupied[-1])
    homo = float(eps[h])
    lumo = np.nan
    for k in range(h + 1, len(eps)):
        if eps[k] != 0.0:
            lumo = float(eps[k])
            break
    return homo, lumo, (lumo - homo if lumo == lumo else np.nan)


def _fock_chunk_elems(median_nbas):
    if median_nbas < 600:
        return 65_536
    if median_nbas <= 2000:
        return 1_000_000
    return 4_194_304


CHUNKS_PER_SHARD = 256


def put_array(g, name, data, codec=DATA_CODEC, chunks=None, overwrite=False):
    """Create array `name` in group `g` holding `data`, using Zarr's sharding codec.

    With sharding an array is a handful of files no matter how many chunks it holds. Without it
    each chunk is a file: the first full run produced 1.5 to 4.7 files per calculation, on course
    to exhaust the 10 M-inode scratch quota. One shard file holds CHUNKS_PER_SHARD chunks (capped
    at the array itself), and the shard length is always a multiple of the chunk length as Zarr
    requires. `codec=None` stores the bytes uncompressed (for incompressible fp32 coefficients).
    """
    data = np.ascontiguousarray(data)
    if chunks is None:
        if data.ndim == 1:
            chunks = (max(1, min(data.shape[0], 1 << 22)),)
        else:
            chunks = (max(1, min(data.shape[0], 1 << 18)),) + data.shape[1:]
    chunks = tuple(int(c) for c in chunks)
    n_chunks = max(1, -(-data.shape[0] // chunks[0]))          # ceil
    shards = (chunks[0] * min(CHUNKS_PER_SHARD, n_chunks),) + tuple(data.shape[1:])
    if overwrite and name in g:
        del g[name]
    z = g.create_array(name=name, shape=data.shape, chunks=chunks, shards=shards,
                       dtype=data.dtype, compressors=codec)
    if data.size:
        z[...] = data
    return z


def write_shard(records, out_dir, shard_name, include_matrices):
    """Write one shard group. Records are parser outputs augmented with calc_id/rel_path/dataset."""
    os.makedirs(out_dir, exist_ok=True)
    path = os.path.join(out_dir, shard_name)
    g = zarr.open_group(path, mode="w")
    n = len(records)
    natom = [r["n_atoms"] for r in records]
    atom_off = np.cumsum([0] + natom).astype("i8")

    def put(name, data, codec=DATA_CODEC, chunks=None):
        put_array(g, name, data, codec=codec, chunks=chunks)

    # ---- identity and column names live in attrs: JSON, portable, no bytes dtype
    g.attrs.update({
        "schema": "omol_elec/pass_a/2",
        "n_calc": n,
        "shells": list(SHELLS),
        "scalar_f8_cols": list(SCALARS_F8),
        "scalar_i_cols": list(SCALARS_I),
        "flag_cols": list(FLAGS),
        "vec_cols": VEC_COLS,
        "atom_f8_cols": ATOM_F8_COLS,
        "atom_shell_cols": SHELL_COLS,
        "pair_names": list(PAIRS),
        "calc_id": [r["calc_id"] for r in records],
        "rel_path": [r["rel_path"] for r in records],
        "dataset": records[0]["dataset"] if n else "",
        "hftyp": [(r.get("hftyp") or "?") for r in records],
        "has_matrices": bool(include_matrices),
    })

    # ---- packed scalars
    sf = np.full((n, len(SCALARS_F8)), np.nan)
    for i, r in enumerate(records):
        for j, k in enumerate(SCALARS_F8):
            v = r.get(k)
            if v is not None:
                sf[i, j] = v
    put("scalar_f8", sf)

    si = np.full((n, len(SCALARS_I)), -1, dtype="i8")
    for i, r in enumerate(records):
        for j, k in enumerate(SCALARS_I):
            v = r.get(k)
            if v is not None:
                si[i, j] = v
    put("scalar_i", si)

    fl = np.zeros((n, len(FLAGS)), dtype="i1")
    for i, r in enumerate(records):
        for j, k in enumerate(FLAGS):
            if k == "is_uhf":
                fl[i, j] = bool(r.get("hftyp") == "UHF")
            elif k == "has_fock":
                fl[i, j] = r.get("fock_a") is not None
            else:
                fl[i, j] = bool(r.get(k))
    put("flags", fl)

    vv = np.full((n, len(VEC_COLS)), np.nan)
    for i, r in enumerate(records):
        c = 0
        for name, w in VEC:
            v = r.get(name)
            if v is not None and len(v) == w:
                vv[i, c:c + w] = v
            c += w
    put("vec", vv)

    # ---- per-atom, packed
    put("atom_offsets", atom_off)
    tot = int(atom_off[-1])
    az = np.zeros(tot, dtype="i2")
    af = np.full((tot, len(ATOM_F8_COLS)), np.nan)
    ash = np.full((tot, len(SHELL_COLS)), np.nan, dtype="f4")
    for i, r in enumerate(records):
        a, b = int(atom_off[i]), int(atom_off[i + 1])
        z = r.get("atomic_numbers")
        if z is not None:
            az[a:b] = np.asarray(z, dtype="i2")
        c = 0
        for k in ATOM_VEC3:
            v = r.get(k)
            if v is not None:
                af[a:b, c:c + 3] = np.asarray(v, dtype="f8").reshape(-1, 3)
            c += 3
        for k in ATOM_1D:
            v = r.get(k)
            if v is not None:
                af[a:b, c] = np.asarray(v, dtype="f8")
            c += 1
        c = 0
        for k in ATOM_SHELL:
            v = r.get(k)
            if v is not None:
                ash[a:b, c:c + len(SHELLS)] = np.asarray(v, dtype="f4").reshape(-1, len(SHELLS))
            c += len(SHELLS)
    put("atom_z", az)
    put("atom_f8", af)
    put("atom_shell", ash)

    # ---- per-pair: one offsets/index/value triple per bond-order flavour
    for key in PAIRS:
        idx, val, offs = [], [], [0]
        for r in records:
            for i, j, v in (r.get(key) or []):
                idx.append((i, j))
                val.append(v)
            offs.append(len(val))
        put(f"pair_{key}_offsets", np.array(offs, dtype="i8"))
        put(f"pair_{key}_index", np.array(idx, dtype="i4").reshape(-1, 2))
        put(f"pair_{key}_value", np.array(val, dtype="f4"))

    # ---- orbitals and matrices (p2 only)
    if include_matrices:
        med = int(np.median([r["nbas"] for r in records])) if n else 1000
        fchunk = _fock_chunk_elems(med)
        for spin in ("a", "b"):
            eps_parts, occ_parts, offs = [], [], [0]
            for r in records:
                e, o = r.get(f"eps_{spin}"), r.get(f"occ_{spin}")
                if e is None:
                    e, o = np.zeros(0), np.zeros(0)
                eps_parts.append(np.asarray(e, dtype="f8"))
                occ_parts.append(np.asarray(o, dtype="f8"))
                offs.append(offs[-1] + len(e))
            put(f"eps_{spin}_offsets", np.array(offs, dtype="i8"))
            put(f"eps_{spin}", np.concatenate(eps_parts) if eps_parts else np.zeros(0))
            put(f"occ_{spin}", np.concatenate(occ_parts) if occ_parts else np.zeros(0))

            fparts, foffs = [], [0]
            for r in records:
                f = r.get(f"fock_{spin}")
                f = np.zeros(0, dtype="i4") if f is None else np.asarray(f, dtype="i4")
                fparts.append(f)
                foffs.append(foffs[-1] + len(f))
            flat = np.concatenate(fparts) if fparts else np.zeros(0, dtype="i4")
            put(f"fock_{spin}_offsets", np.array(foffs, dtype="i8"))
            put(f"fock_{spin}", flat, codec=FOCK_CODEC,
                chunks=(max(1, min(len(flat), fchunk)),))
    return path


def shard_bytes(rec):
    """Rough in-memory footprint, used to decide when to flush a shard."""
    b = 0
    for k in ("fock_a", "fock_b", "eps_a", "eps_b", "occ_a", "occ_b"):
        v = rec.get(k)
        if v is not None:
            b += v.nbytes
    return b + 4096


# ----------------------------------------------------------------------------- reading
def read_calc(g, i):
    """Unpack calculation i from an open shard group into a dict."""
    out = {}
    sf = g["scalar_f8"][i]
    for j, k in enumerate(g.attrs["scalar_f8_cols"]):
        out[k] = float(sf[j])
    si = g["scalar_i"][i]
    for j, k in enumerate(g.attrs["scalar_i_cols"]):
        out[k] = int(si[j])
    fl = g["flags"][i]
    for j, k in enumerate(g.attrs["flag_cols"]):
        out[k] = bool(fl[j])
    vv = g["vec"][i]
    c = 0
    for name, w in VEC:
        out[name] = np.asarray(vv[c:c + w])
        c += w
    out["calc_id"] = g.attrs["calc_id"][i]
    out["rel_path"] = g.attrs["rel_path"][i]
    out["hftyp"] = g.attrs["hftyp"][i]
    out["dataset"] = g.attrs["dataset"]

    a, b = int(g["atom_offsets"][i]), int(g["atom_offsets"][i + 1])
    out["atomic_numbers"] = np.asarray(g["atom_z"][a:b])
    af = np.asarray(g["atom_f8"][a:b])
    cols = g.attrs["atom_f8_cols"]
    out["coords"] = af[:, [cols.index(f"coords_{x}") for x in "xyz"]]
    out["forces"] = af[:, [cols.index(f"forces_{x}") for x in "xyz"]]
    for k in ATOM_1D:
        out[k] = af[:, cols.index(k)]
    ash = np.asarray(g["atom_shell"][a:b])
    for j, k in enumerate(ATOM_SHELL):
        out[k] = ash[:, j * len(SHELLS):(j + 1) * len(SHELLS)]
    for key in PAIRS:
        p0 = int(g[f"pair_{key}_offsets"][i])
        p1 = int(g[f"pair_{key}_offsets"][i + 1])
        out[key] = (np.asarray(g[f"pair_{key}_index"][p0:p1]),
                    np.asarray(g[f"pair_{key}_value"][p0:p1]))
    if g.attrs.get("has_matrices"):
        for spin in ("a", "b"):
            e0 = int(g[f"eps_{spin}_offsets"][i])
            e1 = int(g[f"eps_{spin}_offsets"][i + 1])
            out[f"eps_{spin}"] = np.asarray(g[f"eps_{spin}"][e0:e1])
            out[f"occ_{spin}"] = np.asarray(g[f"occ_{spin}"][e0:e1])
            f0 = int(g[f"fock_{spin}_offsets"][i])
            f1 = int(g[f"fock_{spin}_offsets"][i + 1])
            out[f"fock_{spin}"] = np.asarray(g[f"fock_{spin}"][f0:f1])
    return out


def inflate_fock(tri, nbas):
    """int32 micro-Hartree upper triangle -> symmetric float64 matrix in Eh."""
    M = np.zeros((nbas, nbas))
    M[np.triu_indices(nbas)] = tri.astype(np.float64) * 1e-6
    return M + M.T - np.diag(M.diagonal())


def read_mo(g, i, spin="a"):
    """MO coefficient matrix C[ao, mo] of calculation i for one spin channel (after Pass B1).

    Stored MO-major (C^T) so the occupied block is a contiguous prefix; this returns the
    (nbas, n_stored) matrix with columns = MOs in ORCA AO order: all nbas orbitals in a full-C
    store, the nocc occupied ones in an occupied-only store (see attrs["mo_content"]). Empty
    (nbas, 0) when the channel is absent (beta of an RHF run) or the gbw was not paired.
    """
    nbas = int(g["scalar_i"][i][list(g.attrs["scalar_i_cols"]).index("nbas")])
    o0, o1 = int(g[f"cmo_{spin}_offsets"][i]), int(g[f"cmo_{spin}_offsets"][i + 1])
    if o1 == o0:
        return np.zeros((nbas, 0), dtype=g[f"cmo_{spin}"].dtype)
    # (nbas, nbas) in a full-C store, (nbas, nocc) in an occupied-only one (attrs["mo_content"])
    return np.asarray(g[f"cmo_{spin}"][o0:o1]).reshape(-1, nbas).T


def read_cocc(g, i):
    """Occupied MO coefficients and gbw orbital data for calculation i (after Pass B1).

    Returns C_a (nbas, nocc_a) and C_b (nbas, nocc_b) in ORCA AO order, the occupations of those
    columns, the full gbw orbital energies and occupations, and the B1 flags. Empty arrays when the
    gbw was not paired; check flags['mo_ok'] before trusting the pairing.
    """
    out = {}
    nbas = int(g["scalar_i"][i][list(g.attrs["scalar_i_cols"]).index("nbas")])
    mi = g["mo_i"][i]
    for j, k in enumerate(g.attrs["mo_i_cols"]):
        out[k] = int(mi[j])
    mf = g["mo_f8"][i]
    for j, k in enumerate(g.attrs["mo_f8_cols"]):
        out[k] = float(mf[j])
    fl = g["mo_flags"][i]
    out["flags"] = {k: bool(fl[j]) for j, k in enumerate(g.attrs["mo_flag_cols"])}
    for s in "ab":
        nocc = out[f"nocc_{s}"]
        o0, o1 = int(g[f"cmo_{s}_offsets"][i]), int(g[f"cmo_{s}_offsets"][i + 1])
        if o1 > o0 and nocc:
            # first nocc rows of C^T, read without touching the virtual block
            out[f"C_{s}"] = np.asarray(g[f"cmo_{s}"][o0:o0 + nocc * nbas]).reshape(nocc, nbas).T
        else:
            out[f"C_{s}"] = np.zeros((nbas, 0), dtype=g[f"cmo_{s}"].dtype)
        for name in ("gbw_eps", "gbw_occ"):
            a, b = int(g[f"{name}_{s}_offsets"][i]), int(g[f"{name}_{s}_offsets"][i + 1])
            out[f"{name}_{s}"] = np.asarray(g[f"{name}_{s}"][a:b])
        out[f"cocc_occ_{s}"] = out[f"gbw_occ_{s}"][:nocc]
    return out